To achieve virtual certification for industrial design, quantifying the uncertainties in simulation-driven processes is crucial. We discuss a physics-constrained approach to account for epistemic uncertainty of turbulence models. In order to eliminate user input, we incorporate a data-driven machine learning strategy. In addition to it, our study focuses on developing an a priori estimation of prediction confidence when accurate data is scarce.

翻译：为实现工业设计的虚拟认证，量化仿真驱动过程中的不确定性至关重要。本文讨论了一种物理约束方法，用于处理湍流模型认知不确定性。为消除用户输入，我们采用了数据驱动的机器学习策略。此外，本研究的重点在于开发一种在精确数据稀缺时对预测置信度进行先验估计的方法。